Bibliographic record
Abstract
Since the mid 1990s, lifelong learning has become an extremely fashionable concept. Of course, the idea is not a new one. One leading British adult educator was already writing in 1920 on the topic of “education as a lifelong process” (Yeaxlee 1920: 25). Yet although there is a long standing recognition that learning is a process that continues beyond formal schooling, the level of interest in lifelong learning has shot up since the early 1970s, and in particular since the late 1990s. This development has primarily been associated with policy debate rather than academic interest, and above all it has been fostered by international policy forums. Key founding texts of the first wave of interest in lifelong education include the famous Faure report, published by UNESCO in 1972 (Faure 1972), which was followed by a series of national governmental measures, particularly in Europe, Australasia, Canada and Japan. The second wave of interest was marked by a plethora of major policy documents, starting with the European Commission’s white paper on competitiveness and employment (CEC 1994), followed shortly by further publications from the European Union (CEC 1995, CEC 2000) as well as the Organization for Economic Co-operation and Development (OECD 1996) and the Group of Eight (G8 1999). Once more, the publications of these international policy forums were rapidly followed by a wide range of national policy documents, all of which placed lifelong learning at their centre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".